Predictive modeling tool

ABSTRACT

A system and method of simulating and optimizing industrial and other processes includes a computer that performs multivariate analysis of input variables and output variables to generate a data model of the operation of the process. For industrial applications, the input variables include process variables and the output variables include result variables from the operation of the industrial process. The data model determines contributions to changes in the output or result variables by the respective input or process variables and is provided to a predictive algorithm to identify parameter values for input or process variables expected to have a most significant impact on the output or result variables during performance of the process. The outputs of the predictive algorithm are parameter values that are provided as input or process variables to the industrial process for simulation or performance optimization or product recommendations/optimizations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/250,475, filed Jan. 26, 2021, which is a national stage filing under35 U.S.C. 371 of PCT/US2019/044364, filed Jul. 31, 2019, which claimsthe benefit of U.S. Provisional Application No. 62/713,025, filed Aug.1, 2018, the disclosure of which is incorporated by reference in itsentirety herein.

TECHNICAL FIELD

This application is directed to a predictive modeling tool that uses amultivariate statistical approach and predictive model to driveiterative machine learning applications for simulating and continuouslyoptimizing industrial engineering and other applications.

BACKGROUND

For more than 25 years, spreadsheet-based modeling has been used todescribe and report results for industrial applications. Suchspreadsheets are typically used to note actual performance results andthen to calculate cost/value estimates from the increase or decrease incost. Reports are generated and stored by individuals, typically onlocal servers. However, such an approach has been found to have a lowpredictive or optimization capability and is not particularly useful inreaching solid conclusions.

In a sample industrial engineering application for roll grinding, forexample, tech service support for roll grinding customers is made inspreadsheet files and stored in databases. It is difficult to get anobjective overview of the collected data and the results. Often thereport cannot be presented until after the trials by email.

This limitation is unfortunate, for the business of abrasive bondedproducts, for example, includes custom engineered products where agrinding wheel with 10% higher performance delivers high value in aproduction line. The limiting factor in such industrial systems is tohandle and create a logical structure that may be understood and agreedto by the product manufacturers and the customers. In addition, three tofive years are needed to become an experienced Industrial ApplicationEngineer. If a key Industrial Application Engineer leaves a company, theperformance information and operations knowledge learned on the job istypically lost.

Traditionally, physical simulations or models have been used for productsimulations. By fine tuning the formula coefficients of these models,key performance indicators (KPIs), and settings, it is possible tocreate a data model that well describes the current knowledge. However,physical modelling requires a user to define formulas that describe thecore process relations often via engineering indices and/or keyperformance indicators. The negative aspect of this approach is that onemay only predict/simulate the relations that are already known. It isalso hard to separate an outlier from a point that is new “learning,”i.e., contains new information. This approach requires a company to haveformula and coefficient experts that are motivated to maintain thegenerated physical models. Also, while such physical models tend toallow spreading of available knowledge from the knowledgeable person toa wider group, such models do not have predictive capability.

Statistical approaches have been used in the pharmaceutical industrysince 1978 (Astra Hassle in Sweden) and are today approved and supportedby the FDA. The chemical process industry in Sweden started to use astatistical selection approach in 1985. For the industrial applicationof centerless, centertype, and internal grinding, the company Tyrolitpurportedly has a tool that is used to support optimization at customersites that is used to recommend grinding tools and recommended machinesettings for cylindrical grinding applications. Tyrolit has productselectors that uses logic “trees” for selection of its products.However, to date, statistical machine learning type applications havenot been used for such industrial applications that permit theindustrial applications to be simulated and continuously optimized.

A technical solution is desired that enables a company to maintain andto safeguard its performance data while using the performance data toprovide solid predictive solutions around the world, independent ofapplication engineers or the presence of sales representatives. It isalso desired to create a logical structure that captures the performancedata created during the industrial process for enabling simulation andcontinuous optimization of the industrial process at each customer site.

SUMMARY

Various examples are now described to introduce a selection of conceptsin a simplified form that are further described below in the DetailedDescription. The Summary is not intended to identify key or essentialfeatures of the claimed subject matter, nor is it intended to be used tolimit the scope of the claimed subject matter.

The systems and methods described herein combine modern statisticalanalysis with experience in development, application engineering andsales of industrial products to develop a method for optimization and aweb tool where a new model or application may be added in a very simpleway without changes to web interface coding. A multivariate statisticalapproach is used to increase understanding of the underlying industrialprocesses and to facilitate communication across functional borders. Insample embodiments, the systems and methods described herein support anyiterative/machine learning application to provide recommended machineparameters.

In sample embodiments, a computer-implemented method of implementingpredictive modeling for industrial applications and associatedprocessing apparatus is provided. The method includes a computerperforming multivariate analysis of input variables and output variablesgenerated during the operation of an industrial process to generate adata model of the operation of the industrial process. The inputvariables may include process variables and the output variables mayinclude results from the operation of the industrial process. Theresulting data model represents contributions to changes in the outputvariables by the respective input or process variables. The data modelis provided to a predictive algorithm to identify parameter values forinput or process variables expected to have a most significant impact onselected output variables during performance of the industrial process.The predictive algorithm outputs the parameter values, which areprovided as the input or process variables to the industrial process tooptimize the selected output variables.

In the sample embodiments, the computer generates the data model byautomatically generating a Design of Experiments (DoE) design for aninput variable space of the industrial process where an experimentaldesign is processed by the data model and results are stored based onpreferred results. The predictive algorithm makes predictions of apredetermined number of best next trials for the DoE and outputsparameter values for the predetermined number of best next trials andprobabilities of improved results using the parameter values.

In other sample embodiments, the parameter values and output or resultvariables generated by the industrial process in response to theparameter values are provided as feedback data to the computer foradjustment of the data model. In the sample embodiments, the parametervalues may be output to a display as a simulation of an output of theindustrial process in response to specified input variables. Inaddition, the method may include recommending a product having parametervalues that optimize for the selected output variables in the industrialprocess. The recommended product may optimize the selected outputvariables in the industrial process for at least one of a specifiedmachine, a specified substrate, and a specified application. In othersample embodiments, the method may include identifying products in aportfolio of products that have overlapping parameter values thatoptimize the same selected output variables and/or identifying gaps in aportfolio of products by identifying parameter values that are notrepresented in the portfolio of products for optimizing the selectedoutput variables.

The statistical methods described herein may be applied to a number ofindustrial processes that may be modeled using the DoE methodology. Forexample, the industrial process may be a grinding machine operationwhere the input variables comprise machine settings for a grindingmachine and the output variables depending on a type of grinding processand comprise at least one of G-ratio, material removal rate of agrinding wheel, chip thickness, pieces per dressing cycle, and surfacefinish. In such an embodiment, the machine settings include at least oneof grinding wheel speed, roll speed, traverse speed, continuous infeed,and end infeed grinding time, feed rate, shifting, dressing, dressinginfeed, infeed, and overlap ratio.

In another example, the industrial process may be a product selectionprocess such as an adhesive selection process. In this example, theinput variables comprise selection variables for an adhesive or tape andthe output variables comprise a selection or recommendation of at leastone adhesive or tape. In sample embodiments, the input variables includeat least one of adhesive physical characteristics, adhesive thermalcharacteristics, adhesive electrical characteristics, adhesive curingcharacteristics, adhesive performance characteristics, adhesivedurability characteristics, adhesive chemical resistancecharacteristics, adhesive rheological characteristics, adhesiveviscosity, adhesive setting time, adhesive modulus of elasticity,adhesive solvent resistance, adhesive composition, adhesive dispensingcharacteristics, adhesive use requirements, standardized tests orcertifications, environmental parameters, health parameters, safetyparameters, carrier characteristics, backing characteristics, linercharacteristics, and materials to be bonded by the adhesive or tape. Onthe other hand, the output variables include at least one of tensilestrength, peel strength value, adhesive name, adhesive structuralcharacteristics, adhesive performance characteristics, quantification ofquality of fit, and purchasing information. The adhesive may be apressure sensitive adhesive with or without additional adhesive ornon-adhesive layers.

In other sample embodiments, the industrial process may be a grindingoperation that includes recommending a product that optimizes theselected output variables in the industrial process for at least one ofa specified machine, a specified substrate, and a specified applicationof an abrasive belt.

In other sample embodiments, a method of implementing predictivemodeling is provided in which a computer performs multivariate analysisof input variables relating to characteristics of products and outputvariables relating to performance of the products to generate a datamodel of the products. In the sample embodiments, the data modelrepresents contributions to changes in the output variables by therespective input variables. The data model is provided to a predictivealgorithm to identify parameter values for input variables expected tohave a most significant impact on selected output variables. Thepredictive algorithm outputs the parameter values, and the parametervalues are provided as the input variables to optimize the selectedoutput variables. A product is recommended that optimizes the selectedoutput variables for specified input variables.

In further sample embodiments, the methods include identifying productsin a portfolio of products that have overlapping parameter values thatoptimize the same selected output variables and/or identifying gaps in aportfolio of products by identifying parameter values that are notrepresented in the portfolio of products for optimizing the selectedoutput variables.

In still further sample embodiments, the input variables includecharacteristics of a grinding machine, a bonded abrasive grinding wheel,an abrasive belt, coated abrasive belts or disks, non-woven abrasives,bristle brushes, robot-mounted abrasive articles, an adhesive, and asafety harness. The input variables may further include processvariables of an industrial process and the output variables includeresults from operation of the industrial process. In such embodiments,the data model represents contributions to changes in the outputvariables by the respective input or process variables. The parametervalues are provided as the input or process variables to the industrialprocess to optimize the selected output variables

Further embodiments include a system that implements predictive modelingto optimize an industrial process comprising at least one processor anda memory storing instructions that when executed by the at least oneprocessor cause the at least one processor to perform operationscomprising: performing multivariate analysis of input variables andoutput variables generated during operation of the industrial process togenerate a data model of the operation of the industrial process, theinput variables including process variables and the output variablesincluding results from the operation of the industrial process and thedata model representing contributions to changes in the output variablesby the respective input variables; automatically generating a Design ofExperiments (DoE) design for an input variable space of the industrialprocess; processing an experimental design using the data model;providing results of the processing the experimental design based onpreferred outcome to a predictive algorithm; the predictive algorithmidentifying parameter values for input variables expected to have a mostsignificant impact on selected output variables during performance ofthe industrial process, making predictions of a predetermined number ofbest next trials for the DoE, and outputting parameter values for thepredetermined number of best next trials and probabilities of improvedresults using the parameter values; and providing the parameter valuesas the input variables to the industrial process to optimize theselected output variables.

In such embodiments, the at least one processor further performsoperations comprising feeding the parameter values and output variablesgenerated by the industrial process in response to the parameter valuesas feedback data to the at least one processor and adjusting the datamodel using the feedback data. The at least one processor may furtherperform operations comprising outputting the parameter values to adisplay as a simulation of an output of the industrial process inresponse to specified input variables.

In other embodiments, the at least one processor may further performoperations comprising recommending a product having parameter valuesthat optimize for the selected output variables in the industrialprocess and recommending the product that optimizes the selected outputvariables in the industrial process for at least one of a specifiedmachine, a specified substrate, and a specified application. The atleast one processor may further perform operations comprisingidentifying products in a portfolio of products that have overlappingparameter values that optimize the same selected output variables and/oridentifying gaps in a portfolio of products by identifying parametervalues that are not represented in the portfolio of products foroptimizing the selected output variables.

Any one of the foregoing examples may be combined with any one or moreof the other foregoing examples to create a new embodiment within thescope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. The drawingsillustrate generally, by way of example, but not by way of limitation,various embodiments discussed in the present document.

FIG. 1 illustrates a sample embodiment of a grinding application wheresensors provide feedback data that is processed by a machine learningtool to generate simulation and continuous optimization data.

FIG. 2 is a DModX plot showing the distance of an observation in thetraining set to the input variables in the X model plane or hyper plane.

FIG. 3A is a plot illustrating continuous infeed versus end infeed for agrinding wheel L8 where the labeled area represents a G-ratio of 4.0where the X-axis is a continuous infeed and the Y-axis is an end infeed.

FIG. 3B is a plot illustrating continuous infeed versus end infeed for agrinding wheel M10 where the labeled area represents a G-ratio of 3.2where the X-axis is a continuous infeed and the Y-axis is an end infeed.

FIG. 4A is a plot illustrating how the two products LB-UW and EXL-Pro 8differ in characteristics by the shape of the two groups of productcharacteristics.

FIG. 4B is a plot illustrating a view of the variables of the productsillustrated in FIG. 4A.

FIG. 4C-FIG. 4E are tables that together illustrate test data forgrinding applications.

FIG. 5 is a flow chart illustrating the predictive modeling process insample embodiments.

FIG. 6 illustrates a sample output display showing the predicted valuesfor the parameters of interest in a particular industrial application.

FIG. 7A is a plot illustrating PCA analysis of product properties ofadhesives and tapes.

FIG. 7B is a score plot illustrating the relations between the differentrows or products.

FIG. 8 is a score plot illustrating the possible presence of outliers,groups, similarities, and other patterns in the data.

FIG. 9 is a pq plot that is a superimposition of the p plot and the qplot for the first and second predictive components of the orthogonalprojections to latent structures (OPLS)/Two-way Orthogonal PLS (O2PLS)model.

FIG. 10 is the Normal Probability Plot of Residuals where the residualsare standardized on a double log scale.

FIG. 11 illustrates a handheld device that measures the surface freeenergy of a surface of a substrate.

FIG. 12 illustrates the FTIR spectra of two polypropylene samples.

FIG. 13 illustrates the workflow for applying the predictive modelingtechnique to the adhesive products selection process in sampleembodiments.

FIG. 14 is a chart illustrating the average measured peel versus theaverage predicted peel for a variety of substrate/adhesive combinations.

FIG. 15 illustrates a chart of sample abrasive belts byapplication-substrate-machine in sample embodiments.

FIGS. 16A-16B are loadings plots illustrating the information gatheredin FIG. 15 converted into loadings plots for the recommended productsfor different machines/substrates, where FIG. 16B is a zoomed in versionof FIG. 16A.

FIGS. 17A-17B are charts illustrating the qualitative information fromFIG. 15 converted into numerical ‘rankings’ indicating suitability of agiven machine/substrate for a particular application on a scale of 1-12.

FIG. 18 is a plot illustrating the observed versus predicted beltapplications for robotic applications.

FIG. 19 is a plot illustrating the observed versus predicted beltapplications by machine (and pressure).

FIGS. 20A-20B are charts illustrating the refined numerical rankings forthe refined data (taking pressure into account).

FIG. 21 is a table illustrating the performance ratings for roboticsmachine applications and products.

FIG. 22 is a plot illustrating the observed versus predicted results fora particular Product-Application-Machine/Pressure to provide acorresponding performance rating.

FIG. 23 is a table illustrating the modeled rating based on numericalrating and shows that the information from each predicted data point maybe reviewed.

FIG. 24 is a table including a product list of harness data thatcontains potentially hundreds of products with potentially thousands ofstock keeping units (SKUs) worldwide for providing to the predictivemodeling tool to provide product portfolio rationalization.

FIG. 25 is a plot illustrating a preliminary predictive model of theharness data of FIG. 24 as generated using a multivariate statisticalanalysis tool that plots the correlations among the collectedparameters.

FIG. 26 illustrates a general-purpose computer that may be programmedinto a special purpose computer suitable for implementing one or moreembodiments of the system described in sample embodiments.

DETAILED DESCRIPTION

It should be understood at the outset that although an illustrativeimplementation of one or more embodiments are provided below, thedisclosed systems and/or methods described with respect to FIGS. 1-26may be implemented using any number of techniques, whether currentlyknown or in existence. The disclosure should in no way be limited to theillustrative implementations, drawings, and techniques illustratedbelow, including the exemplary designs and implementations illustratedand described herein, but may be modified within the scope of theappended claims along with their full scope of equivalents.

The methodology described below may be used for a number of industrialprocesses or industrial engineering applications, the rapid pricing ofcomplex engineered products used in such industrial processes orindustrial engineering applications, medical treatments, and the like.As used herein, “industrial process” or industrial engineeringapplication” is intended to encompass a number of technological fieldapplications including resin-bond wheels (such as roll grinding, tool orflute grinding, face grinding, rail grinding wheels, hot-pressed wheels,portable or mounted cut-off wheels, depressed center grinding wheels,cut-and grind wheels, flex wheels, cup wheels, snagging wheels),vitrified bond grinding wheels (such as gear grinding, cylindrical(ID/OD/Centerless, Cam/Crank grinding wheels, surface/creep feedgrinding wheels, face grinding wheels), super-abrasive grinding wheels(for tool grinding, cam/crank grinding, surface and creep feed grinding,cylindrical ID/OD, profiles, turbines and gear grinding), coatedabrasive belts or disks, non-woven abrasives, bristle brushes,robot-mounted abrasive articles, adhesive selection, and even for morediverse industrial products such as safety harness products, smartfilters, product recommenders and selectors, portfolio rationalizers fordetermining which overlapping product offerings to consolidate,applications to research and development to identify areas for targetedresearch, product price calculators (including value calculators),training, and the like. Samples described herein include the simulationand/or optimization of a grinding application and an interactivemodeling process for selecting a correct adhesive for a particularapplication by collecting test data relating to input variables andmaterials to be bonded by the adhesive and measurable output variables.Other samples described herein include a product recommender forabrasive belts, and a portfolio optimizer and product recommender forsafety harness products. The simulation, optimization, productselection, product recommendation, and portfolio optimization featuresmay be applied to any suitable industrial process within the scope ofthe present disclosure.

As will be apparent from the following detailed description, thepredictive modeling tool described herein addresses the above-mentionedand other technical shortcomings in the prior art by providingapplication engineers with a simple software application on a smartphoneor handheld computer (e.g., iPad) in the field enabling them to changemultiple machine parameters at the same time and to work with customersto obtain a desired outcome (e.g., performance optimization). Thepredictive modeling tool described herein also enables the equipmentprovider to build and to maintain knowledge about the operation of itsequipment and to reach maximum product performance.

Industrial Engineering Application Example—Grinding Tools

FIG. 1 illustrates a sample embodiment of a grinding application wheresensors provide feedback data that is processed by a machine learningtool to generate simulation and continuous optimization data. In thisembodiment, a substrate 10 is ground by a grinding wheel 12 undercontrol of a servo motor 14. In turn, the servo motor 14 is controlledby a main controller 16 that automates the operation of the servo motor14 and grinding wheel 12. During operation, sensors 18 (e.g., speed,torque, environmental, temperature, pressure, rotation, vibration,imaging sensors, etc.) provide feedback regarding the operation of thegrinding wheel 12 to an ancillary controller 20 that may, asappropriate, provide feedback control signals to the servo motor 14and/or the main controller 16 to adjust the operation of the grindingoperation based on sensor feedback. In sample embodiments, the feedbacksensor data is stored during operation in cloud storage 22 and providedto a machine learning tool 24 that learns the operation of the grindingwheel 12 under different conditions. As will be explained in more detailbelow, the machine learning tool 24 learns the optimal operatingconditions of the grinding wheel 12 for different operating conditionsand wheel specifications and provides the corresponding parameters tothe ancillary controller 20 and/or main controller 16 to providecontinuously optimizing control during the operation of the grindingwheel 12. Also, the stored operating parameters may be used to simulateoperation of the grinding wheel 12 on a display 26 in response tospecified inputs. The operating parameters may also be provided to anapp or web page accessible using a user's (e.g., application engineer's)smartphone or handheld computer 28 to assist the user with the settingof the control parameters for the grinding wheel 12.

Those skilled in the art of machine learning and artificial intelligence(AI) applications will appreciate that the machine learning tool learnsoptimized settings that evolve over time and constantly improve thegrinding performance. The secured application data from all completedtests and operations is stored in the cloud data storage 22 to maintaina continuous record of the continuous optimization performed by themachine learning tool 24. It will be further appreciated that the storedsimulations make product training much easier and significantly shorterfor new sales reps and application engineers as well as for machinebuilders or end user customers for their own setup and use of theproducts. Such simulations also assist application engineers and/orproduct developers in the development of the grinding tools to afinished usable application in line with the simulations.

The test data for “training” the machine learning tool 24 is obtained byperforming a number of tests of the grinding apparatus. In a test, amaster data file containing 98 grinding tests for 19 instances of use ofa 3M™ Cubitron™ II Resin-bond Roll Grinding Wheel containing 3M™Precision Shaped Grain (PSG) over 29 different test occasions were usedto generate a predictive data model. 83 data points and 19 variableswere used in the data model with a predictive ability of Q′=0.63, whichindicates a good predictive level.

A web application also has been created with an adaptable/modifiableinterface to load new grinding models into the user's smartphone 28,thus providing a very rapid and simple process to add new grindingapplications. Although the model only contains data based on 3M™Cubitron™ II Resin-bond Roll Grinding Wheels, verification of the modelresults was made for any wheels containing crushed abrasives. Five testinstances came out with +/−0, +20% and 3×>30% improvement compared toexisting settings, illustrating the robustness and versatility of themodeling tool. Also, as explained below, an optimization tool has beendeveloped that supports simultaneous change of multiple variables. Thisoptimization tool provided a significant increase in speed of theoptimization process. Such rapid optimization allows movement from thepoint of operation in the direction of the optimum machine settingdefined by the model and calibration dataset, which decreases risk ofdamaging a product or machine, increases customer value by each step,and provides a safer and more stable work process. Those skilled in theart will appreciate that the optimum point is individualized for eachmachine/customer and may vary over time. The optimum point is reached bystepwise approach or by the limits of parameter settings in the machine.

The gathered data may also be used to assist users of the grinding wheel12 to validate their generated data by creating a scores plot toposition the user in relation to a user database in cloud data storage22 and to identify the most relevant test reports. The machine learningtool 24 or other processing elements in the cloud data storage 22 maythen, based on the user supplied data, validate correctness of the usersupplied data or settings for the maximum or best possible performanceand evaluate the degree of “stretch” in the user data in relation to theuser database using a distance to model plot 30 as shown in FIG. 2. FIG.2 is a DModX plot showing the distance of an observation in the trainingset to the input variables in the X model plane or hyper plane. DModX isproportional to the residual standard deviation (RSD) of the inputvariables (X) observation. By default, DModX is displayed in normalizedunits, that is, the absolute DModX divided by the pooled residualstandard deviation of the model. The critical value of DModX, denotedDcrit, is computed from an F-distribution, also known as Snedecor's Fdistribution or the Fisher-Snedecor distribution which is, inprobability theory and statistics, a continuous probabilitydistribution. Dcrit regulates the size of the envelope surrounding thedata points of the training set.

Also, based on the user supplied data, performance may be simulated andthe test wheel specification (i.e., hardness, structure and percent 3M™Precision Shaped Grain (PSG), G-ratio) may be defined. For example, FIG.3A is a plot illustrating a grinding wheel L8 where the area 32represents a G-ratio of 4.0 where the X-axis is a continuous infeed andthe Y-axis is an end infeed, and FIG. 3B is a plot illustrating agrinding wheel M10 where the area 34 represents a G-ratio of 3.2. Theoutput thus provides a defined test wheel specification. It will beappreciated by those skilled in the art that the G-ratio target isideally as high as possible for maximum performance.

Once the grinding wheel parameters have been selected, a user referencerun with the test grinding wheel may be run. The generated data is savedto a temporary test space and to the global database in the cloud datastorage 22 by selecting “save data point” and entering the X (input) andY (result) parameters. Data points may be saved for a number ofcompetitor grinding wheels to generate a global database by selecting“save data point” and manually entering the X and Y parameters. Insample embodiments, variables are selected that may be changed in theoptimization (i.e., wheel speed, roll speed, traverse speed, continuousand end infeed). Results variables to be optimized (i.e., G-ratio andremoval rate of grinding wheel) are also selected. The selectedvariables are then provided to the machine learning tool 24 to generateperformance predictions. In a sample embodiment, the machine learningtool 24 automatically generates a face centered cubic (FCC) Design ofExperiments (DoE) (where instead of just in the corners and middle thereare also experiments in the middle of the faces), makes predictions, andpresents the three best next trials based on the results andprobabilities of improved results using the parameter values in theresults. One test is then selected, and the test is re-run. The saveddata points during the re-run are saved and transferred to a “valuecalculation template,” and the results of the value calculation arereviewed, edited, and submitted as a test report to the user.

Usually DoE is used to minimize the number of experiments and maximizethe information output in order to minimize work effort. Under idealconditions, it is possible to understand contributions from 256variables from 16 trials. In sample embodiments, the trials are notperformed but simulations are run to calculate results in the different“experimental points.” DoE is used to maximize efficiency in search ofthe optimum point in an n-dimensional process window. The more pointsand the better the design, the higher the accuracy. In sampleembodiments, a 3-level full factorial was used, although otherexperimental designs may be used. In an even more extensive design, moredata points than the 3-level full factorial may be used whereexperiments are also added on the middle of all edges.

By collecting grinding data in this fashion, statistical tools such asmultivariate analysis and a partial least squares (PLS)/orthogonalprojections to latent structures (OPLS) analysis may be used by themachine learning tool 24 to extract information from historic grindingdata (or Design of Experiment (DoE)-test grindings) to create modelsdescribing variable contributions to performance with good predictiveability. For example, in the case of roll grinding, 63 variables wereavailable in the primary data and 19 of these variables carriedinformation that contributed to prediction of performance, in this case,the grinding ratio of the grinding wheel (G-ratio=grinding ratio,defined as volume of material removed per volume of wheel wear. G thusincreases with less wheel wear and/or higher material removal). Datafrom the 98 grinding results from the 3M™ Cubitron™ II Resin-bond RollGrinding Wheel mentioned above were in the data set. 83 of these pointsqualified for use in creation of the data model. The key elements in themultivariate analysis are the ability to extract/separate informationand noise and the handling of qualitative variables.

To optimize machine parameters of the roll grinding machine, 14variables were selected to describe the machine, roll or workpiece andthe grinding wheel. 5 variables were selected to describe the machinesettings. By using a 3 level-full factorial DoE on the selected 5machine parameters, a design window was defined. The model was then usedto predict performance for the 243 resulting machine settingalternatives. The highest 10-20 were then presented to the user toselect the setting to use.

By storing the selected settings and including a step where the user(e.g., application engineer) reports back the actual result, new data isgenerated that may then be used to improve the underlying model and theprediction. In this manner, the machine learning tool 24 continuouslyimproves its data and its predictive capability.

In conventional manual and physical optimization, it is a very commonstrategy to change one parameter at a time and sometimes to iterate toidentify the next settings to be evaluated. However, when changing oneparameter at a time, there is a risk that local limitations may forcethe user to stop earlier than if multiple parameters were changedsimultaneously. By contrast, the approach described herein has theadvantage that the model utilizes all the information existing in thehistoric data and allows or even encourages changes of multipleparameters simultaneously to point to the optimum set of parameters. Inthis way, the probability of finding a point of operation that hashigher performance than iterative changes is significantly higher.

Five tests have been made with a data model based on the 3M™ Cubitron™II Resin-bond Roll Grinding Wheel data. At machine number 1, improvementwas not possible since the grinding wheel was too soft. Machine number 2had been an optimization object for 5 years with no progress. Use of themachine learning tool provided a 20% improvement in G-ratio when threeparameters were changed simultaneously. Machine numbers 3, 4, and 5 allexhibited a +30% improvement in G-ratio together with improved cycletime.

The advantage of using the machine learning tool 24 for simulation andoptimization applications as described herein is that it may predictimproved product usage and pricing based on historic business operationsand then make consequent predictions of non-existing materials/variants.In many cases, industrial products such as bonded abrasive products areclassified according to different safety standards such as burst speed.Burst tests have been useful to provide data to then predict burst speedof bonded abrasives with successful outcomes. This has the potential toreduce the need for destructive testing and automating the assignment ofmaximum operating speed to the bonded abrasive products. Burst tests areconventionally performed using automated burst test equipment, and wheelselection for burst testing follows the regional standards, such asANSI, EN, and JIS. To automate such a process, an understanding of thedifferent product groups and how they relate to each other must beunderstood based on differences in characteristics. For example, inbonded abrasives, such characteristics may include abrasive groups andspecifications. A very simple example of product map may include datasuch as the existing product name, dimensions, accessories, systemcomponents, force, speed, and grinding process used.

The resulting data may be graphed to illustrate how products differ fromeach other in characteristics. For example, FIG. 4A is a plotillustrating the analysis of how the two-wheel grinding products LB-UWand EXL-Pro 8 differ in characteristic by the shape of the two groups(LB-UW is shown at 36 and EXL-Pro 8 is shown at 38). FIG. 4B is a plotillustrating a view of the variables 40 of the products illustrated inFIG. 4A. If the plots of FIG. 4A and FIG. 4B are considered together, itbecomes apparent that variable wheel speed and speed of movement ishigher while variable force is lower for the LB-UW products. This datawas found to correlate to a longer life expectance for the LB-UW productcompared to the EXL-Pro 8 product. FIG. 4A and FIG. 4B together alsoillustrate that a constructive discussion on different productcharacteristics is much facilitated by utilizing the type of analysisdescribed herein.

As another example, when a high number of products are used in the sameapplications but on different materials or with different machinesettings, the analysis described herein may be used to enablevisualization to see where there are overlapping products or gaps tofill. When launching a new product and utilizing the machine learningpredictive tools described herein, it will very rapidly be possible todescribe which products may be replaced in the existing portfolio andwhich characteristics are truly unique for the new product. In the caseof roll grinding application engineer tool data, the data may also beused to analyze and understand the customer base. For example, a scoreplot may be used to show customers their “patterns” in their machinesettings and the performance results achieved. Looking at the data froma variables perspective may be used to isolate the G-ratio as well ashard to grind or easy to grind materials. Also, a new product may get tomarket faster if the product may be tested with selected customers inthe scores swarm and in a structured way define in what group thatproduct would provide the largest benefits. As the knowledgebase growsand models improve, the machine learning tool 24 may be used to moreaccurately provide correct specifications for customer needs and toprovide correct and relevant machine settings. Over time, theknowledgebase so developed will become key to development andimprovement of the industrial tools.

The statistical approach described herein creates general data modelsfrom data, and variables, that contribute to predictive ability. Asnoted in the above example, the statistical model approach may be usedto predict grinding performance. An advantage with this approach is thestatistical analysis of values and model ability (i.e., modelconfidence, error source for outliers, identify new objects withdifferent characteristics, the efficient visualization of data, etc.) isautomatically included. However, it will be appreciated by those skilledin the art from the above example and the following description of FIG.5 that corresponding data models may be generated from other types ofindustrial processes using the techniques described herein.

Predictive Model

FIG. 5 illustrates a generalized predictive modeling process 42 in asample embodiment. As illustrated, the predictive modeling process 42starts by inputting test data into a spreadsheet such as Xcel at 44.Such test data depends on the industrial application. For example, ingrinding applications, the test data may include machine type, machineage, machine power, and grinding parameters such infeed, wheel speed,roll speed, etc. as illustrated in FIGS. 4C-4E. The more test data thatis provided, the more accurate the resulting prediction may be expectedto be. Any data of a confidential nature would only be evaluatedinternally if necessary for use in the model. In sample embodiments, upto 30 Design of Experiments (DoE) tests may be used to generate the testdata. As known to those in the field of statistics, DoE is a method tochange variables in a structured way to quantify variable contributionsto responses and by designing/selecting the experiments carefully sothat variable interactions, exponential and non-linear relations may bequantified and utilized to produce a model with predictive ability,usually with a minimum number of experiments. A DoE tests involves notonly the selection of suitable independent, dependent, and controlvariables, but planning of the delivery of the experiment understatistically optimal conditions given the constraints of availableresources. In sample embodiments, such experimental setups are providedto enable the generation of suitable input variables and outputvariables for generation of a data model of the operation of theindustrial process of interest. Typically, the input variables includeprocess variables and the output variables including result variablesfrom the operation of the industrial process, and the resulting datamodel represents contributions to changes in the output or resultvariables by the respective input or process variables in accordancewith the experimental arrangement.

A data model is created at 46 from the collected statistical test datausing the statistical techniques described in more detail below. Thedata model is then provided to model development software and modelruntime engine, such as the SIMCA Q.DLL math engine at 48. The modeldevelopment software and model runtime engine include a predictivealgorithm that generates the predictions for the specified inputvariables expected to have the most significant impact on the outputvariables affecting performance of the grinding application. Inoperation, the model development software and model runtime engine 48performs a pre-treatment process for the centering and scaling ofvariables including the Y-space scores vector(s) and the X-space scoresvector(s). Through calculation of the transfer vector between X-spacevectors and Y-space vectors, the predictive model is defined. Thepost-treatment process (i.e., reverse centering and scaling ofvariables) includes running sample data or the DoE data through themodel and presenting the variables and results. The predictive algorithmis then a function of the model, the DoE, and the tests surrounding it,Q2, R2, Scores, Loadings, Hotellings, and DmodX, where the tests providemetrics on data structure and model capability.

A graphics interface is generated, and the prediction results areprovided graphically in a web interface at 50. In sample embodiments,the web interface display includes several variable/machine parametersand output performance values such as G-ratio or surface finish that areadapted for presentation in a graphics display (in a WINDOWS™ or otherapplication interface format) such as the graphics display 26 or thedisplay in a software application of a user's smartphone 28 (FIG. 1).

A sample output display is illustrated in FIG. 6 showing the values forthe predicted parameters 54 for the machine settings of interest and thebest possible performance values 56 (e.g., G-ratio) for the machinesettings in the particular industrial application. The user then usesthese values to manually update the machine settings and to provide themachine settings as the input or process variables to the industrialprocess to optimize selected output variables at 52. In the illustratedexample of a grinding wheel application, such machine settings mayinclude G-ratio, material removal rate of a grinding wheel, chipthickness, pieces per dressing cycle, surface finish, speed of grindingwheel, speed of roll, traverse speed of grinding wheel in relation toroll, continuous infeed, end infeed, grinding time, feed rate, shifting,dressing, dressing infeed, infeed, and overlap ratio and the like.Alternatively, the adjustments may be automatic whereby thevariables/machine parameters are provided directly to the ancillarycontroller 20 or main controller 16 in the embodiment of FIG. 1 toautomatically control the operation of the servo motor 14. The updatedmachine settings may then be fed back to update the predictive model at48. Alternatively, as will be explained below with respect to otherembodiments, the predictions optionally may be used to provide productrecommendations and/or to optimize product portfolios by identifyingholes and/or overlaps in product lines at 53.

Thus, the predictive model of FIG. 5 may be used to process inputs andoutputs relating to an industrial process, create a data model for thedata, generate predictions from the data model, and provide predictionsof best possible results (e.g., best machine settings) based on testdata. Users may change multiple parameters (e.g., wheel specification,speed, etc.) at the same time and make adjustments until a desiredoutput has been developed (e.g., best possible G-ratio while eliminatingredundant iterations that do not lead to performance improvements). Inthe above roll grinding example, generic test data is collected (e.g.,into spreadsheet) and data is collected relating to the roll (e.g., rolldiameter, roll width), the wheel (e.g., wheel diameter, wheel width,abrasive grit hardness, hardness number, structure, bond, etc.), and thegrinding machine parameters (e.g., wheel speed, roll speed, table speed,continuous infeed, end infeed, number of passes rough, chip thickness,special material removal rate, speed ratio wheel/roll, overlap ratio,used machine power, machine pressure, machine current, etc.) and thetest results (e.g., G-ratio, surface roughness, grinding time). The datamodel is created from the statistical data using multivariate analysisand processed using a math engine, and the data model is used togenerate predictions and graphics for display in a web-basedapplication. The prediction results are displayed on a display (FIG. 6).Simultaneous changes of multiple variables and machine parameters arepermitted to accelerate prediction of the best possible results andrecommended machine settings based on the test data. New models may beloaded to address new applications as well as new industrial processes.Further examples are provided below.

Data Model Creation

The creation of the predictive model at 46 in FIG. 5 may use any of anumber of methods known to those with a background in statisticalanalysis and/or linear algebra. For example, the model creator starts byclarifying the test objective and reviewing the dataset. In the rollgrinding example above, 98 rows of grinding test data from 29 differenttests occasions were used. The variables to support the objective arethen grouped and an initial principal component analysis is run on thedata. The resulting Scores, Loadings, DModX, Hotellings, and residuals(described below) are reviewed. The variables are also reviewed toidentify information and patterns that are not described in the dataset.New variables are generated as appropriate. A principal componentanalysis is run on the updated dataset. Any changes that improve themodel fit, R2, are kept. R2 is the percent of variation of the trainingset—Y with orthogonal projections to latent structures (OPLS)—explainedby the Y-predictive components. R2 is a measure of fit, i.e., how wellthe model fits the data. A large R2 (close to 1) indicates a good modelbut it is not sufficient, for a poor predictive model may still have alarge R2. A poor R2 is also obtained when one has poor reproducibility(much noise) in the training set, or when for other reasons X does notexplain Y.

Output or result (Y-variables) are introduced as appropriate, and themodel is run according to instructions, often algorithm for a partialleast squares (PLS) or OPLS analysis is used to produce the models. Theresulting Scores, Loadings, DModX, Hotellings and residuals are againreviewed and observed versus a predicted plot. A variable importanceplot is generated and reviewed. Expanded cross/square variables areadded as well as a transformation of variables as recommended whilestepwise generating new models and keeping changes that improve themodel fit R2 and/or predictive ability Q2 of the dataset. Q2 is thepercent of variation of the training set—Y with OPLS—predicted by themodel according to cross-validation. Q2 indicates how well the modelpredicts new data. A large Q2 (Q2>0.5) indicates good predictivity. Apoor Q2 is obtained when the data has much noise, or when therelationship X to Y is poor, or when the model is dominated by a fewscattered outliers. In the process described herein, outliers(observations) in the data are typically removed and variables notuniquely contributing to improved predictive ability are removed by aniterative process to generate the model. The variable plots are againreviewed to confirm the contributions of the variables. The process isstopped when improvements in R2 and Q2 give sufficiently minor changes,for example, in hundredths or thousands of a percentage point.

Those skilled in the art will appreciate that PLS regression is astatistical method that bears some relation to principal componentsregression. However, instead of finding hyperplanes of maximum variancebetween the response and independent variables, it finds a linearregression model by projecting the predicted variables and theobservable variables to a new space. Because both the X and Y data areprojected to new spaces, the PLS family of methods are known as bilinearfactor models.

PLS is used to find the fundamental relations between two matrices (Xand Y), i.e. a latent variable approach to modeling the covariancestructures in these two spaces. The PLS model tries to find themultidimensional direction in the X space that explains the maximummultidimensional variance direction in the Y space. PLS regression isparticularly suited when the matrix of predictors has more variablesthan observations, and when there is multicollinearity among X values.

More information about a sample prediction process that may beincorporated herein may be found in a book entitled “Multi andMegavariate Data Analysis,” 3rd revised edition, L. Eriksson, T Byrne, EJohansson, J Trygg, C Wikström and thus will not be further describedherein.

Predictions and Optimizations

Once R2 and Q2 have been calculated, the work file is saved. All workmodels are removed and only the last data model is saved as a modeldescription file. The resulting data model may be used to makepredictions and/or simulations of the system from which the dataoriginates, in this case, a grinding model. For example, once the datamodel has been created at 46, the model description file containing thedata model for the application is then called by the model developmentsoftware and model runtime engine at 48. In a sample embodiment, codeautomatically sends the test/machine settings to the model developmentsoftware and model runtime engine, which makes predictions by runningthe data model through its predictive algorithm as described above.

Optimization is then performed by defining variables to optimize,typically the machine variables. Ranges are defined for each variable.High/low settings are used where the process is known to be stable andthe operator of the machine being optimized is comfortable. In theexample noted above, a DoE matrix was generated, a 3-level fullfactorial (3 levels and 5 variables give 3⁵ (243) differentcombinations), and the DoE matrix was run through a simulation model.Variable combinations were sorted based on performance/value/result inthe response variables as appropriate. Preferred variable settings wereselected and a run was made to confirm results.

The machine learning tool 24 also implements a “self-learning process”that provides continuous optimization. By saving the actual resultsafter the optimization to the initial model/calibration/datafile, a newmodel may be produced which includes learning and information fromtrials that have just been performed. If the new data points improvemodel fit and/or predictive ability, they may be added, and a new modelgenerated. Initially this step is manual, but since variablecontributions and transformations are likely not to change, this stepmay be automated and greatly simplified compared to the processdescribed above. If the model fit and/or predictive ability decrease,review of the variable contributions needs to be conducted to identifynew variables/interactions/transformations to determine if theobservation is an outlier or an observation containing new information.

Simulations

Once a data model has been created, simulations of different operatingconditions of the industrial process may be generated to show thepredicted results for specified input machine parameters withoutactually having to run the industrial process. The simulation results sogenerated may be displayed on display 26 or sent to the operator'ssmartphone 28 for display (see FIG. 1).

Statistical Analysis

The test data to generate the data model and to predict outcomes basedon changes to different variables is generated using statisticalanalysis. Short definitions of the terminology useful in understandingthe statistical analysis applied in sample embodiments is describedbelow.

Partial Least Squares Projections to Latent Structures

Partial Least Squares (PLS) is used to relate the result variables tothe input and process variables in sample embodiments to address theproblem of identifying those input and process variables (X) that are“responsible” for changes in the output or result variables (Y). Forthis purpose, multiple regression may be used. However, multipleregression leads to great difficulties because process data usually doesnot possess the correct properties for regression modeling as regressiondeals with each result variable (y_(m)) separately. Therefore, one endsup with a set of models, one for each output of interest. This makesinterpretation and optimization difficult or impossible. To allow astrict interpretation of cause and effect, the data is collected in acareful experimentation process using statistical DoE design, usingsoftware such as MODDE®. To search for relationships between input andoutput in process logs is risky and often less successful because aprocess does not provide data with good information content when theimportant factors are well controlled within small control intervals.

Partial Least Squares—Scores

PLS modeling has been developed for situations with numerous,often-correlated input and process variables with several to many resultvariables. To use PLS, one specifies which variables in the database arepredictors (X) and which variables are dependent (Y). PLS then finds therelation between the variables X and Y. The PLS model is expressed as aset of X-score vectors, Y-score vectors, X-weight vectors, and Y-weightvectors for a set of PLS model dimensions. Each dimension (index a)expresses a linear relation between an x-score vector (t_(a)) and aY-score vector (u_(a)). The weight vectors of each model dimensionexpress how the X-variables are combined to form t_(a) and how theY-variables are combined to form u_(a). In this way, the data aremodeled as a set of factors in X and Y and their relationships. Plots ofthe scores and weights facilitate interpretation of the model.

Partial Least Squares—Loadings

The PLS analysis results in model coefficients for the variables, calledPLS-weights or loadings. The loadings for the X-variables, denoted w,indicate the importance of these variables, i.e., how much they in arelative sense participate in the modeling of Y. The loadings for theY-variables, denoted by c, indicate which Y-variables are modeled in therespective PLS model dimensions. When these coefficients are plotted ina w*c plot, a picture is obtained that shows the relationships between Xand Y, those X-variables that are important and those Y-variables thatare related to which X-variable, etc.

Partial Least Squares—Residuals

PLS provides residuals on both the results (Y-side) and on the input(X-side). The standard deviation of these residual distances may beplotted just as for principal components analysis to provide a thirdstatistical process control plot showing if the process is behavingnormally or not in the DModX and DModY plots.

Principal Component Analysis

Principal Component Analysis (PCA) of a data table gives vectors ofscores, with values T_(ia), which summarize all the variables enteringthe analysis. It is customary to calculate two or three score vectorsand then to plot them against each other to generate tt-plots. Thett-plots give a picture that is the best summary of the process behaviorover time. The tt-plots allow one to see trends, unusual behavior, andother things of interest. The number of components is decided by thecomponent contribution to the model's predictive ability or toindividual component contributions to an individual results variable.When contributions are lower than a threshold, then generation ofcomponents is stopped. With experience, it is possible to recognize anarea in the PCA score plot in which the process remains under “normal”operation to provide a multivariate control chart. The score plot incombination with the loading plot together indicate the responsiblevalues for deviations from normal operation.

For example, FIG. 7A and FIG. 7B are loadings plots illustrating PCAanalysis of product properties (e.g., modulus of elasticity, tensilestrength, viscosity, toughened, polyacrylic, abraded, elongation atbreak, FRP, PVC, open time, etc.) 58 of adhesives and tapes (FIG. 7A)where the scores plot of FIG. 7B illustrates the relations between thedifferent rows or products (e.g., DP100 Clear, DP100FR White, DP190Gray, DP270 Black, DP105 Clear, DP 100 Plus Clear, DP110 Gray, DP405Black, DP100FR White, etc.) 60. Each product is positioned according toits unique combination of properties. The loadings plots of FIG. 7A andFIG. 7B show how the properties/columns relate to each other and providethe basis for a product selector or a translation table. The PCA alsoprovides residuals, deviations between the data and the PCA model, namedDModX. When these residuals are large, this indicates an abnormalbehavior in the process. To see this, a plot of the residual standarddeviation, DModX (residual distance, root mean square) is provided. TheDModX plot may be used to identify data outliers. This indicates thatthese observations are different from then normal observations withrespect to the correlation structure of the variables.

In the case of principal components analysis, it will be appreciatedthat variables with the same correlation pattern are grouped. The groupof variables with the highest correlation to the results parameters(price or performance) become the 1st principal component and the secondhighest correlated group becomes the 2nd principal component. Principalcomponents are added until criteria for contributions to predictiveability or model fit are no longer met. On the other hand, in the caseof application mapping or product selector applications, there is noresults parameter. The variables, characteristics, uses, andapplications are plotted as principal components and the products andapplications are plotted in positions corresponding to the mix ofvariables and characteristics that make up the specific observation.These and other variations will become apparent to those skilled in theart of statistical analysis.

Orthogonal Projections to Latent Structures (OPLS)

OPLS is an extension of PLS and addresses the regression problem. OPLSseparates the systematic variation in X into two parts, one part that iscorrelated (predictive) to Y and one part that is uncorrelated(orthogonal) to Y. This gives improved model interpretability. In thesingle-Y case, there is only one predictive component, and allcomponents beyond the first one reflect orthogonal variation. However,with multiple Y-variables, there may be more than one predictive OPLScomponent.

Two-way Orthogonal PLS (O2PLS) is another extension of PLS thataddresses the data integration problem. In the two-block (X/Y) context,O2PLS examines which information overlaps between the two data tablesand which information is unique to a specific data table (X or Y). O2PLSaccomplishes this task by a flexible model structure incorporating threetypes of components, that is:

(i) Components expressing the joint X/Y information overlap;

(ii) Components expressing what is unique to X; and

(iii) Components expressing what is unique to Y.

For both OPLS and O2PLS, the different components are interpretable theusual way, since the scores, loadings, and residual-based parameterswith a familiar meaning are preserved.

Scores Scatter Plot t1 Versus t2

The scores t1, t2, etc. are new variables summarizing the X-variables.The scores are orthogonal, i.e., completely independent of each other.There are as many score vectors as there are components in the model.The score t1 (first component) explains the larges variation in the Xspace, followed by t2, etc. Thus, the scatter plot of t1 versus t2 is awindow in the X space displaying how the X observations are situatedwith respect to each other. The score plot in FIG. 8 shows the possiblepresence of outliers, groups, similarities, and other patterns in thedata. The score plot 58 is a map of the observations. With atwo-dimensional score plot 58, the model development software and modelruntime engine draws the tolerance ellipse 60 based on Hotelling's T2.Observations situated far outside the ellipse are outliers as, forexample, observation 39 (the rightmost dot) in FIG. 8.

Observations with a DModX twice as large as the critical value of DModX(Dcrit) are moderate outliers. This indicates that these observationsare different from the normal observations with respect to thecorrelation structure of the variables. A moderate outlier may beinterpreted by selecting it and opening up a corresponding contributionplot that displays the residuals of all X-variables. Variables withlarge positive or negative residuals are those differing with respect tothe systematic structure captured by the model.

Hotelling's T2 Plot

The Hotelling's T2 (column or line) plot displays the distance from theorigin in the model plane (score space) for each selected observation.The plot shows the T2 calculated for the range of selected components,i.e., 1 to 7, or 3 to 6. Values larger than the 95% confidence limit aresuspect, and values larger than the 99% confidence limit may beconsidered as serious. A large T2 range value for a given observation,i.e., a value far above the critical limits, indicates that theobservation is far from the other observations in the selected range ofcomponents in the score space. Hence, it is likely to be an outlyingobservation that, if in the training set, may pull the model in adetrimental way. T2 is basically calculated as the sum over the selectedrange of components of the scores in square divided by their standarddeviations in square. Thus, T2 is the distance in the model plane (scorespace) from the origin, in the specified range of components.

When one or several training set observations with large T2 range valuesare seen, the operator may look also at the scores, DModX, andcontribution plots of the same model and the same range of components tohelp understand why the observations have high T2 range values. It isnoted that a set of points marked in the T2 plot will also be marked inthe score and DModX plots.

Loadings Scatter Plot pq1 Versus pq2 OPLS/O2PLS

The OPLS/O2PLS loadings plot displays the relationship between theX-variables and the Y-variables for the predictive components. Theloadings p correspond to the X-part of the model and the loadings qcorrespond to the Y-part of the model. To facilitate interpretation,this plot is color coded according to the model terms for X and Y. Thepq plot of FIG. 9 is a superimposition of the p plot and the q plotfirst and second predictive components of the OPLS/O2PLS model. Theloadings p correspond to the covariances between the X-variables and thepredictive score vectors t, whereas the loadings q correspond to thecovariances between the Y-variables and the predictive score vectors u.X and Y variables with large p or q contribute strongly to the model. Itis thus possible to see how the Y-variables vary in relation to eachother, which ones provide similar information, and their relationship tothe X-variables in the model.

Normal Probability Plot of Residuals

As illustrated in FIG. 10, the Normal Probability Plot of Residualsdisplays the residuals standardized on a double log scale. Thestandardized residual is the raw residual divided by the residualstandard deviation (RSD). The plot of FIG. 10 enables one to detectoutliers and to assess the normality of the residuals. If the residualsare random and normally distributed, the normal probability plot of theresiduals has all the points lying on a straight line 62 between −4 and+4 standardized standard deviations. Experimental runs lying outside the−4 or +4 standard deviations are outliers.

As explained further below, the predictive model and associatedstatistical analysis may be applied to data generated by any of a numberof industrial processes to provide product selection tools, productrecommender tools for particular applications, and portfolio optimizersfor product portfolio rationalization.

Industrial Engineering Application Example—Adhesive Selection Tool

Currently, a large portion of the adhesive products selection processfor customer applications is performed based on the experience of theapplication engineer. The performance of the bond will depend on theinteraction between the adhesive and the substrate. The adhesivebehavior, whether the adhesive is a pressure sensitive adhesive with orwithout additional adhesive or non-adhesive layers, is usually wellcharacterized; however, the substrate nature may vary greatly dependingon a large number of parameters such as surface finish, bulk polymeradditives, surface treatment (corona, primer, etc.), aging, storagecondition, and the like. Thus, adhesive selection provides anotherindustrial environment where the predictive modeling tool describedherein may process collected data to predict adhesive performance tohelp accelerate and to improve the adhesive selection process.

In sample embodiments, handheld instruments may be used for thecollection of needed data from the substrate surface. Such datacollection may be performed at the customer site or any other convenientlocation. For example, the surface free energy of a surface is aparameter that dictates the performance of an adhesive/substrate. Thesurface free energy is calculated using the contact angles of two knownliquids and by measuring the contact angle of the droplets on thesurface of the substrate. For example, FIG. 11 illustrates a handhelddevice 1100 that measures the surface free energy of a surface of asubstrate 1110. The device of FIG. 11 uses a liquid droplet on a solidsurface to measure the contact angle.

Fourier Transform Infrared (FTIR) spectroscopy also may be used tomeasure parameters of an adhesive/substrate in sample embodiments asFTIR spectroscopy is ideally suited to determine the identity of apolymeric material. FTIR spectroscopy is a technique used to obtain afar-infrared spectrum of absorption or emission of a solid, liquid orgas by measuring how much light a sample absorbs at each wavelength. AnFTIR spectrometer simultaneously collects high-spectral-resolution dataover a wide spectral range by pressing a sample against a diamondcrystal to collect absorption data. For example, FIG. 12 illustrates theFTIR spectra 1200 and 1210 of two polypropylene samples. In FTIRspectroscopy, a Fourier transform is used to convert the raw data intothe actual spectrum of the type illustrated in FIG. 12.

Other measurable input variables for an adhesive/substrate may includeone or more of the following: adhesive physical characteristics,adhesive thermal characteristics, adhesive electrical characteristics,adhesive curing characteristics, adhesive performance characteristics,adhesive durability characteristics, adhesive chemical resistancecharacteristics, adhesive rheological characteristics, adhesivecomposition, adhesive dispensing characteristics, adhesive userequirements, standardized tests or certifications, environmentalparameters, health parameters, safety parameters, carriercharacteristics, backing characteristics, linear characteristics, andmaterials to be bonded by the adhesive.

As in the above grinding example, multivariate analysis also may be usedto understand how the different factors influence each other as well asto determine the main causes of variations in a dataset. Also, as in theabove grinding example, Partial Least Squares (PLS) may be used toprocess the data originating from analytical instruments (e.g., FTIR,nuclear magnetic resonance (NMR), etc.) to construct predictive models.

The workflow for applying the predictive modeling technique describedherein to the adhesive products selection process is summarized in FIG.13. As illustrated, the X-variables representing the substrateparameters are collected using a variety of measurement techniques suchas FTIR spectroscopy the create a database 1300 including the parametersfor respective substrates such as polypropylene, polyethylene,polycarbonate, stainless steel, aluminum, red paint, nylon, glass,polymethylmethacrylate (PMMA), black acrylonitrile butadiene styrene(ABS), polyvinylchloride (PVC), etc. and for respective adhesiveproducts such as adhesive tapes (e.g., by part number). The Y-variablesmay be collected, for example, by performing peel tests (e.g., a 3MIndustrial Adhesives and Tapes Division (IATD) 90° peel test) at 1310 toestablish a main response for the adhesive tape on the respectivesubstrates. This type of analysis may be used to create a predictivemodel at 1320 using statistical packages such as Unscrambler, Umetrics,and R based on a set of variables that influence measurable outputparameters. For example, the performance of the tape/substratecombination evaluated using a standard peel strength test may be used asthe main response. The resulting output variables may include name ofthe recommended adhesive, adhesive structural characteristics, adhesiveperformance characteristics, quantification of quality of fit, and/orpurchasing information. The two surfaces of interest are analyzed usingthe chosen techniques (surface energy and FTIR spectroscopy) and used asvariables in the statistical packages to create the predictive model1320 as described with respect to FIG. 5. The predictive model 1320 isthen tested by testing the substrate at 1330, and the process isiterated using additional samples to improve the predictive model 1320as an iterative process over time.

FIG. 14 is a chart illustrating the average measured peel versus theaverage predicted peel for a variety of substrate/adhesive combinations.As illustrated, the predictive model was fairly accurate using thedeveloped dataset. However, the Lexan samples 1400 show that thepredictive model 1320 was confused by a similar type of chemistry whencompared to the reference. Such inaccuracy in the predictive model 1320may be improved over time by using more samples to build the predictivemodel 1320. In this example, the adhesive product with the desired peelstrength value for the substrate would be selected.

Industrial Engineering Application Example—Product Recommender forAbrasive Belts

Abrasive belts are offered in a wide range of constructions (e.g.,backing, mineral type and size, etc.) for different applications andpressure regimes. In order to offer a tool for global applicationengineers, sales force and customers, the predictive modeling techniquesdescribed herein may be used to develop a table to group‘process-substrate-machine’ and match them with the agreed upon productrecommendations. As with the other examples, the predictive modelingprocess starts by building a predictive model to predict the bestproduct recommendation for a specific customer situation(process-substrate-machine).

The product finder/recommender logic is based on multivariate analyticsto provide continuous refinement of the predictions as soon as new userdata is added to the base data set. As in the above examples, apredictive model is built based on existing user data from productdeveloper & application engineers (and customers). The predictive modeldetermines and recommends the next best (closest matching) product basedon the ‘distance’ between the customer need and the existing productportfolio offering. In sample embodiments, the product finder isweb-based and is accessible by computers and other hand-held processingdevices such as tablets, laptops, smartphones, etc. In sampleembodiments, the product finder is implemented on a common architecturefor recommender systems to provide standardized functionality.

FIG. 15 illustrates a chart 1500 of sample abrasive belts byapplication-substrate-machine. FIG. 15 shows the applications 1510 andtypical grades 1520 along the left-hand side of the chart 1500 and thesubstrate 1530 and machine 1540 along the top of the chart 1500 asdetermined by the predictive model. In FIG. 15, the cells 1540 in chart1500 are displayed in different colors to identify the best valueoptions based on experimentation, the best options for applications thatmay struggle to show value, applications that are considered an unlikelyapplication for the indicated machine/substrate, and applications thatare not possible, so no recommendation is made. The rankings thusillustrate the tool with the highest performance for each combination ofapplication and substrate. FIGS. 16A-16B are loadings plots illustratingthe information gathered in FIG. 15 converted into loadings plots forthe recommended products 1600 for different machines/substrates, whereFIG. 16B is a zoomed in version of FIG. 16A.

FIGS. 17A-17B are charts illustrating the qualitative information fromFIG. 15 converted into numerical ‘rankings’ indicating suitability of agiven machine/substrate for a particular application on a scale of 1-12.

FIG. 18 illustrates a graph of the observed versus predicted beltapplications for a particular application, in this case, roboticapplications. As illustrated in FIG. 18 for robotic applications, thebest observed versus prediction results are provided for themachine/substrate/applications that fall on the line 1800.

As a next step, the pressure was considered (high-medium-low), as wellas the contact wheel, where the pressure is correlated to machine type.FIG. 19 illustrates a graph of the observed versus predicted beltapplications by machine (and pressure) where, once again, the bestobserved versus prediction results are provided for themachine/substrate/applications that fall on the line 1900. FIGS. 20A-20Bare charts illustrating the refined numerical rankings for the refineddata (taking pressure into account).

FIG. 21 is a chart illustrating the performance ratings 2100 forrobotics machine applications 2110 and products 2120. In FIG. 21, thepressure is also correlated to machine type.

FIG. 22 illustrates a graph of the observed versus predicted results fora particular Product-Application-Machine/Pressure to provide acorresponding performance rating. For example, the highest rating forweld cleaning and through-feed is predicted for the circled example 2200provided at the right-hand side of the chart. To confirm if thepredictions align with actual experience, the information from FIG. 22may be converted into a table for review by the application engineer.For example, FIG. 23 is a chart illustrating the modeled rating based onnumerical rating and shows that the information from each predicted datapoint may be reviewed. For example, the arrow 2300 shows the predictedratings. Such use of the data from the consumer will increaseproductivity by helping the customer to analyze the available data.

It will be appreciated that the product recommender may be separatedinto three phases: product finder/recommender, R&D design tool, andproduct selector and sales guide. In sample embodiments, the applicationengineer uses the product recommender to identify the appropriateabrasive belt for a particular application. For different markets,changes to specific terms not used in that market may be identified toadjust the product recommender as appropriate. On the other hand, theproduct recommender may be used by customers to identify which productto choose based on the machine, substrate and process being operated bythe customer. In the R&D design tool phase, the test data from theabrasive belt manufacturing process may be collected and used to predictthe products that will provide the best results. The product selectormay further be applied to sales data collected and/or reported duringthe sales process to recommend the best product based on, e.g., aFinish-Application-Substrate-Tool (FAST).

Product Portfolio Rationalization Example—Portfolio Optimizer andProduct Recommender for Safety Harness Products

The following embodiment offers an example of how the predictivemodeling tool described herein may be used as a tool for productportfolio rationalization, white space identification, and productrecommendation. As in the above embodiments, a multivariate analysistool is used to analyze data generated from safety harness productofferings and used to assist the business organization with portfoliorationalization by enabling visualization of groups of products ofsimilar performance or characteristics. Though described with respect tosafety harness products, it will be appreciated the techniques describedherein may be used with any of a number of products where there is avariety of product offerings with similar and potentially overlappingcharacteristics. The same techniques may be used to simplify productportfolios and may be further used to develop a productselector/recommender as in the abrasive belts embodiment describedabove.

This embodiment addresses the situation where, for a variety of reasons,a company may provide many products (e.g., fall protection products)available under multiple brands and legacy businesses that offerproducts with overlapping features. In such situations, it is oftendifficult for application engineers and customers to know which productis most suitable for a specific application requirement. On the otherhand, it would be very beneficial for the business organization tounderstand which products may be grouped together in an effort toprepare for a global product portfolio rationalization.

As in the other embodiments, the process starts by collecting productdata for the predictive modeling process. For example, as illustrated inFIG. 24, a product list may contain potentially hundreds of productswith potentially thousands of stock keeping units (SKUs) worldwide. Itwill be appreciated that the more SKUs, the more helpful the predictivemodeling tool is for providing product portfolio rationalization. In theexample of FIG. 24, qualitative harness data 2400 is provided. In thisexample, the absence of quantitative data allows for product groupingbased on criteria such as application, harness Type, D-ring Location,and buckle type or other criteria that are qualitatively significant fordifferentiating the product.

FIG. 25 is a plot illustrating a preliminary predictive model of theharness data of FIG. 24 as generated using the multivariate statisticalanalysis tool Simca by Umetrics that plots the correlations 2500 amongthe collected parameters. To provide a visualization of the productgroupings, 14 settings were defined with a random generator. FIG. 25illustrates the outcome as the visualization of product groupings withsimilar features and properties. Using a ‘harness scoring’, the modelscores the closest product model that fits the predictive model for theidentified settings. If one product does not have 100% fit, thepredictive model will show the closest fit. Then, once several productgroups are clustered together, the clustered data may be evaluated toidentify the top N closest harnesses. Those product groupings may beused to identify product clusters of similar properties, enablingmeaningful portfolio rationalization. On the other hand, for a productrecommender, it may be more important to limit the selection criteria toa manageable number, for if customers have too many options, they maystop looking for a product.

This embodiment thus uses the predictive modeling tool to identify gapsand overlaps in product lines by monitoring the fit of the output of thepredictive model to the input feature parameters. Analyzing thevariables and the order in which the variables were selected by acustomer may be utilized to understand customer preferences/choices.

Enabling customer selection of a safety product also brings in otherconsiderations. For example, it may be required by regulations, etc. towarn a user that the selected safety product may not be a perfect matchfor particular applications. In such cases, key performance criteria mayneed to be the key considerations for safety products before otherfeatures are taken into account. A mandatory field may need to be addedto the product data indicating for which standard a product is approvedand to limit selection of that product to the applications under theidentified standard. Also, the product recommender may highlight areasof greater concern for a particular application of a product. Forexample, a product color may be less important than arc flashrating/high energy discharge and other features critical to safety totrigger a warning. For example, the selection of a particular productfor a particular application may pop up a notification: ‘This selectionmatches the selected safety critical variables by only 90%, not 100%,less than perfect match.’ The product recommender thus enables thevendor to identify and to fill gaps in its product portfolio and tooffer custom products while reducing liability to its customers orvendors.

The systems and methods described herein thus implement multivariatestatistical analysis and machine learning techniques to predictperformance of industrial products from simple to increasingly complexdata files identifying the product characteristics and desired outcomesor features for particular applications of the industrial products. Thepredictive modeling tool may be used by customers to search for theappropriate product for a particular application and by companies torecommend products for particular applications as well as to rationalizeproduct portfolios to find holes/overlaps in product offerings. Theseand other applications will be apparent to those skilled in the art fromthe above descriptions of illustrative embodiments.

Computer Embodiment

FIG. 26 illustrates a typical, general-purpose computer that may beprogrammed into a special purpose computer suitable for implementing oneor more embodiments of the system disclosed herein. The controllers 16and 20 and machine learning tool 24 described above may be implementedon any general-purpose processing component, such as a computer withsufficient processing power, memory resources, and communicationsthroughput capability to handle the necessary workload placed upon it.The processing component 70 includes a processor 72 (which may bereferred to as a central processor unit or CPU) that is in communicationwith memory devices including secondary storage 74, read only memory(ROM) 76, random access memory (RAM) 78, input/output (I/O) devices 80,and network connectivity devices 82. The processor 72 may be implementedas one or more CPU chips or may be part of one or more applicationspecific integrated circuits (ASICs).

The secondary storage 74 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 78 is not large enough tohold all working data. Secondary storage 74 may be used to storeprograms that are loaded into RAM 78 when such programs are selected forexecution. The ROM 76 is used to store instructions and perhaps datathat are read during program execution. ROM 76 is a non-volatile memorydevice that typically has a small memory capacity relative to the largermemory capacity of secondary storage 74. The RAM 78 is used to storevolatile data and perhaps to store instructions. Access to both ROM 76and RAM 148 is typically faster than to secondary storage 74.

The devices described herein may be configured to includecomputer-readable non-transitory media storing computer readableinstructions and one or more processors coupled to the memory, and whenexecuting the computer readable instructions configure the processingcomponent 70 to perform method steps and operations described above withreference to FIG. 1 to FIG. 25. The computer-readable non-transitorymedia includes all types of computer readable media, including magneticstorage media, optical storage media, flash media and solid-statestorage media.

It should be further understood that software including one or morecomputer-executable instructions that facilitate processing andoperations as described above with reference to any one or all of stepsof the disclosure may be installed in and sold with one or more serversand/or one or more routers and/or one or more devices within consumerand/or producer domains consistent with the disclosure. Alternatively,the software may be obtained and loaded into one or more servers and/orone or more routers and/or one or more devices within consumer and/orproducer domains consistent with the disclosure, including obtaining thesoftware through physical medium or distribution system, including, forexample, from a server owned by the software creator or from a servernot owned but used by the software creator. The software may be storedon a server for distribution over the Internet, for example.

Also, it will be understood by one skilled in the art that thisdisclosure is not limited in its application to the details ofconstruction and the arrangement of components set forth in thefollowing description or illustrated in the drawings. The embodimentsherein are capable of other embodiments, and capable of being practicedor carried out in various ways. Also, it will be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items. Unless limited otherwise, the terms“connected,” “coupled,” and “mounted,” and variations thereof herein areused broadly and encompass direct and indirect connections, couplings,and mountings. In addition, the terms “connected” and “coupled” andvariations thereof are not restricted to physical or mechanicalconnections or couplings. Further, terms such as up, down, bottom, andtop are relative, and are employed to aid illustration, but are notlimiting.

The components of the illustrative devices, systems and methods employedin accordance with the illustrated embodiments of the present inventionmay be implemented, at least in part, in digital electronic circuitry,analog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. These components may beimplemented, for example, as a computer program product such as acomputer program, program code or computer instructions tangiblyembodied in an information carrier, or in a machine-readable storagedevice, for execution by, or to control the operation of, dataprocessing apparatus such as a programmable processor, a computer, ormultiple computers.

A computer program may be written in any form of programming language,including compiled or interpreted languages, and it may be deployed inany form, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program may be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network. Also, functional programs,codes, and code segments for accomplishing the present invention may beeasily construed as within the scope of the invention by programmersskilled in the art to which the present invention pertains. Method stepsassociated with the illustrative embodiments of the present inventionmay be performed by one or more programmable processors executing acomputer program, code or instructions to perform functions (e.g., byoperating on input data and/or generating an output). Method steps mayalso be performed by, and apparatus of the invention may be implementedas, special purpose logic circuitry, e.g., an FPGA (field programmablegate array) or an ASIC (application-specific integrated circuit), forexample.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general-purpose processor, a digital signalprocessor (DSP), an ASIC, a FPGA or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random-access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of example,semiconductor memory devices, e.g., electrically programmable read-onlymemory or ROM (EPROM), electrically erasable programmable ROM (EEPROM),flash memory devices, and data storage disks (e.g., magnetic disks,internal hard disks, or removable disks, magneto-optical disks, andCD-ROM and DVD-ROM disks). The processor and the memory may besupplemented by or incorporated in special purpose logic circuitry.

Those of skill in the art understand that information and signals may berepresented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill in the art further appreciate that the variousillustrative logical blocks, modules, circuits, and algorithm stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application,but such implementation decisions should not be interpreted as causing adeparture from the scope of the present invention. A software module mayreside in random access memory (RAM), flash memory, ROM, EPROM, EEPROM,registers, hard disk, a removable disk, a CD-ROM, or any other form ofstorage medium known in the art. An exemplary storage medium is coupledto the processor such the processor may read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium may be integral to the processor. In other words, the processorand the storage medium may reside in an integrated circuit or beimplemented as discrete components.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)), and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store processor instructions. Theterm “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions for execution by one or more processors, such that theinstructions, when executed by one or more processors cause the one ormore processors to perform any one or more of the methodologiesdescribed herein. Accordingly, a “machine-readable medium” refers to asingle storage apparatus or device, as well as “cloud-based” storagesystems or storage networks that include multiple storage apparatus ordevices. The term “machine-readable medium” as used herein excludessignals per se.

The above-presented description and figures are intended by way ofexample only and are not intended to limit the illustrative embodimentsin any way except as set forth in the appended claims. It is noted thatvarious technical aspects of the various elements of the variousexemplary embodiments that have been described above may be combined innumerous other ways, all of which are considered to be within the scopeof the disclosure.

Accordingly, although exemplary embodiments have been disclosed forillustrative purposes, those skilled in the art will appreciate thatvarious modifications, additions, and substitutions are possible.Therefore, the disclosure is not limited to the above-describedembodiments but may be modified within the scope of appended claims,along with their full scope of equivalents.

What is claimed is:
 1. A computer-implemented method of implementingpredictive modeling, comprising: a computer performing multivariateanalysis of input variables relating to characteristics of products andoutput variables relating to performance of the products to generate adata model of the products, the data model representing contributions tochanges in the output variables by the respective input variables;providing the data model to a predictive algorithm to identify parametervalues for input variables expected to have a most significant impact onselected output variables, the predictive algorithm outputting theparameter values; providing the parameter values as the input variablesto optimize the selected output variables; and recommending a productthat optimizes the selected output variables for specified inputvariables.
 2. The method of claim 1, wherein the data model comprisesthe computer automatically generating a Design of Experiments (DoE)design for an input variable space of the industrial process, processingan experimental design using the data model and storing results based onpreferred outcome, further comprising the predictive algorithm makingpredictions of a predetermined number of best next trials for the DoEand outputting parameter values for the predetermined number of bestnext trials and probabilities of improved results using the parametervalues.
 3. The method of claim 1, further comprising identifyingproducts in a portfolio of products that have overlapping parametervalues that optimize the same selected output variables.
 4. The methodof claim 1, further comprising identifying gaps in a portfolio ofproducts by identifying parameter values that are not represented in theportfolio of products for optimizing the selected output variables. 5.The method of claim 1, wherein the input variables comprisecharacteristics of at least one of a grinding machine, a bonded abrasivegrinding wheel, an abrasive belt, coated abrasive belts or disks,non-woven abrasives, bristle brushes, robot-mounted abrasive articles,an adhesive, and a safety harness.
 6. The method of claim 1, wherein theinput variables include process variables of an industrial process andthe output variables include results from operation of the industrialprocess, and the data model represents contributions to changes in theoutput variables by the respective input or process variables, whereinthe parameter values are provided as the input or process variables tothe industrial process to optimize the selected output variables
 7. Themethod of claim 1, wherein the industrial process is a grinding machineoperation, the input variables comprising machine settings for agrinding machine and the output variables depend on a type of grindingprocess and comprise at least one of G-ratio, material removal rate of agrinding wheel, chip thickness, pieces per dressing cycle, and surfacefinish.
 8. The method of claim 1, wherein the machine settings includeat least one of grinding wheel speed, roll speed, traverse speed,continuous infeed, end infeed, grinding time, feed rate, shifting,dressing, dressing infeed, infeed, and overlap ratio.
 9. The method ofclaim 1, wherein the industrial process is an adhesive selectionprocess, the input variables comprising selection variables for anadhesive or tape and the output variables comprising a selection orrecommendation of at least one adhesive or tape.
 10. The method of claim9, wherein the input variables include at least one of adhesive physicalcharacteristics, adhesive thermal characteristics, adhesive electricalcharacteristics, adhesive curing characteristics, adhesive performancecharacteristics, adhesive durability characteristics, adhesive chemicalresistance characteristics, adhesive rheological characteristics,adhesive viscosity, adhesive setting time, adhesive modulus ofelasticity, adhesive solvent resistance, adhesive composition, adhesivedispensing characteristics, adhesive use requirements, standardizedtests or certifications, environmental parameters, health parameters,safety parameters, carrier characteristics, backing characteristics,liner characteristics, and materials to be bonded by the adhesive ortape.
 11. The method of claim 9, wherein the output variables include atleast one of tensile strength, peel strength value, adhesive name,adhesive structural characteristics, adhesive performancecharacteristics, quantification of quality of fit, and purchasinginformation.
 12. The method of claim 1, wherein the industrial processis a grinding operation, further comprising recommending a product thatoptimizes the selected output variables in the industrial process for atleast one of a specified machine, a specified substrate, and a specifiedapplication of an abrasive belt.
 13. A system that implements predictivemodeling to select a product for an industrial process, comprising: atleast one processor; and a non-transitory memory storing instructionsthat when executed by the at least one processor cause the at least oneprocessor to perform operations comprising: retrieving input variablesrelating to characteristics of a set of products; retrieving outputvariables relating to performance of each of the set of products;generating a data model of the products, the data model representingcontributions to changes in the output variables by the respective inputvariables; performing multivariate analysis of the input variables andoutput variables to generate a data model of the operation of theindustrial process, and the data model representing contributions tochanges in the output variables by the respective input variables;automatically generating a Design of Experiments (DoE) design for aninput variable space of the industrial process; processing anexperimental design using the data model; providing results of theprocessing the experimental design based on preferred outcome to apredictive algorithm; the predictive algorithm identifying parametervalues for input variables expected to have a most significant impact onselected output variables during performance of the industrial process,making predictions of a predetermined number of best next trials for theDoE, and outputting parameter values for the predetermined number ofbest next trials and probabilities of improved results using theparameter values; providing the parameter values as the input variablesto the industrial process to optimize the selected output variables; andrecommending a product that optimizes the selected output variables forspecified input variables; and a controller that generates a graphicsinterface that comprises the product recommendation.
 14. The system ofclaim 13, wherein the at least one processor recommending the productfurther comprises the at least one processor performing operationscomprising recommending the product that optimizes the selected outputvariables in the industrial process for at least one of a specifiedmachine, a specified substrate, and a specified application.
 15. Thesystem of claim 13, wherein the at least one processor further performsoperations comprising identifying products in a portfolio of productsthat have overlapping parameter values that optimize the same selectedoutput variables.
 16. The system of claim 13, wherein the at least oneprocessor further performs operations comprising identifying gaps in aportfolio of products by identifying parameter values that are notrepresented in the portfolio of products for optimizing the selectedoutput variables.